How has the increase of Automated Trading Systems (ATS) influenced the futures market?
\[ \text{Performance} = \text{Skill} \times \sqrt{\text{Breadth}} \]
Two extremes
Single bet: \(0.51 \times 1000 + 0.49 \times (-1000) = 20\)
Multi bet: \(1000 \times [0.51 + 0.49 \times (-1)] = 20\)
The same expected return
Single bet: 49%
Multi bet: \(0.49 \times 0.49 \times \dots \times 0.49 = 0.49^{1000} \approx 0\)
\[ \text{risk} := \text{std}\left\{1,-1,-1,1, \dots, 1 \right\} = 1 \]
\[ \begin{align} \text{risk} &:= \text{std}\left\{1000,0,0,0, \dots, 0 \right\} = 31.62 \\ \text{risk} &:= \text{std}\left\{-1000,0,0,0, \dots, 0 \right\} = 31.62 \end{align} \]
Just like Sharpe Ratio
Single bet: \(\text{SR}_{\text{single}} = \frac{20}{31.62} =0.63\)
Multi bet: \(\text{SR}_{\text{multiple}} = \frac{20}{1} =20\)
\(20 = 0.63 \times \sqrt{1000}\)
\(\text{SR}_{\text{multiple}} = \text{SR}_{\text{single}} \times \sqrt{\text{Bets}}\)
\(\text{Performance} = \text{Skill} \times \sqrt{\text{Breadth}}\)
We use insights gained from years of fundamental trading to inspire bespoke quantitative strategies that are applied to a large collection of commodity markets
How to obtain a stationary time series
| Statistic | BB1 | BB2 |
|---|---|---|
| Annualized Return | 0.055 | 0.192 |
| Annualized Sharpe (Rf=0%) | 0.662 | 1.239 |
| Annualized Std Dev | 0.083 | 0.155 |
| Average Negative Month Return | -0.016 | -0.028 |
| Average Positive Month Return | 0.019 | 0.044 |
| Maximum Drawdown | 0.267 | 0.395 |
| Maximum Drawdown/Annualized Return | 4.869 | 2.058 |
| Number of Negative Months | 103.000 | 95.000 |
| Number of Positive Months | 147.000 | 154.000 |
Literature on extracting carry from futures:
Literature on applying machine learing techniques in algorithmic trading:
| Statistic | TR1 |
|---|---|
| Annualized Return | 0.189 |
| Annualized Std Dev | 0.178 |
| Annualized Sharpe (Rf=0%) | 1.064 |
| Maximum Drawdown | 0.267 |
| Maximum Drawdown/Annualized Return | 1.410 |
| Number of Positive Months | 142.000 |
| Number of Negative Months | 107.000 |
| Average Positive Month Return | 0.057 |
| Average Negative Month Return | -0.035 |
Strategy not yet live.
Strategy not yet live.
| Statistic | 1998- | 2008- | 2015- |
|---|---|---|---|
| Annualized Return | 0.200 | 0.191 | 0.061 |
| Annualized Sharpe (Rf=0%) | 1.706 | 1.654 | 0.658 |
| Annualized Std Dev | 0.117 | 0.115 | 0.093 |
| Average Negative Month Return | -0.021 | -0.023 | -0.023 |
| Average Positive Month Return | 0.041 | 0.042 | 0.029 |
| Maximum Drawdown | 0.187 | 0.137 | 0.128 |
| Maximum Drawdown/Annualized Return | 0.936 | 0.718 | 2.085 |
| Number of Negative Months | 100.000 | 54.000 | 23.000 |
| Number of Positive Months | 155.000 | 82.000 | 29.000 |
| Statistic | S&P500 | PS Multi Strategy | PS Multi Strategy and S&P500 |
|---|---|---|---|
| Annualized Return | 0.129 | 0.154 | 0.145 |
| Annualized Sharpe (Rf=0%) | 1.139 | 1.524 | 1.931 |
| Annualized Std Dev | 0.113 | 0.101 | 0.075 |
| Average Positive Month Return | 0.028 | 0.029 | 0.022 |
| Avereage Negative Month Return | -0.024 | -0.017 | -0.014 |
| Number of Negative Months | 32.000 | 34.000 | 27.000 |
| Number of Positive Months | 64.000 | 62.000 | 69.000 |
| Worst Drawdown | 0.170 | 0.069 | 0.070 |
Combining a
to investing in commodities we create a product with
that gives superior risk adjusted returns.